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Improving Small-Scale CMB Lensing Reconstruction

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arxiv 1905.04217 v1 pith:FYH7QRPO submitted 2019-05-10 astro-ph.CO

classification astro-ph.CO
keywords lensingmethodsmall-scaletemperaturebackgrounddataestimatorfactor
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abstract

Over the past decade, the gravitational lensing of the Cosmic Microwave Background (CMB) has become a powerful tool for probing the matter distribution in the Universe. The standard technique used to reconstruct the CMB lensing signal employs the quadratic estimator (QE) method, which has recently been shown to be suboptimal for lensing measurements on very small scales in temperature and polarization data. We implement a simple, more optimal method for the small-scale regime, which involves taking the direct inverse of the background gradient. We derive new techniques to make continuous maps of lensing using this "Gradient-Inversion" (GI) method and validate our method with simulated data, finding good agreement with predictions. For idealized simulations of lensing cross- and autospectra that neglect foregrounds, we demonstrate that our method performs significantly better than previous quadratic estimator methods in temperature; at $L=5000-9000$, it reduces errors on the lensing auto-power spectrum by a factor of $\sim 4$ for both idealized CMB-S4 and Simons Observatory-like experiments and by a factor of $\sim 2.6$ for cross-correlations of CMB-S4-like lensing reconstruction and the true lensing field. We caution that the level of the neglected small-scale foreground power, while low in polarization, is very high in temperature; though we briefly outline foreground mitigation methods, further work on this topic is required. Nevertheless, our results show the future potential for improved small-scale CMB lensing measurements, which could provide stronger constraints on cosmological parameters and astrophysics at high redshifts.

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  1. On likelihood-based analysis of the gravitationally (de)lensed CMB

    astro-ph.CO 2025-02 accept novelty 7.0 of 10

    The exact lensing spectrum score is a posterior-averaged quadratic estimator on delensed maps with realization-dependent debiasing, plus a small 2-point term degenerate with CMB spectra.

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